Features

Emphasizes the practical application and interpretation of results rather than focusing on the theory behind the analyses

Offers an introduction to R, including a summary of its most important features

Contains many examples and exercises

Summary

A Proven Guide for Easily Using R to Effectively Analyze Data

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.

A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution

New examples and additional exercises in several chapters

A new version of the HSAUR package (HSAUR2), which is available from CRAN

This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.

Author(s) Bio

Brian S. Everitt is Professor Emeritus at King’s College, University of London.

Torsten Hothorn is Professor of Biostatistics in the Institut für Statistik at Ludwig-Maximilians-Universität München.

Reviews

I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians.—International Statistical Review (2011), 79

… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find. … —MAA Reviews, April 2011

Praise for the First Edition…Brian Everitt has joined forces with a recognized expert who displays an impressive command of this powerful environment … Much is to be learned in the small details that make this text interesting even for experienced users. … Special attention is given to graphical methods … —Journal of Applied Statistics, May 2007

Useful examples are presented to assist understanding. … Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. … I highly recommend the text for anyone learning R and who want to use it for the sophisticated analysis of data.—Joseph M. Hilbe, Journal of Statistical Software, Vol. 16, August 2006

…a useful, compact introduction.—Biometrics, December 2006

… This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. … a very valuable reference. …The book is particularly good at highlighting the graphical capabilities of the language. …—P. Marriott, ISI Short Book Reviews

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